GPU Accelerated Particle Visualization with Splotch

Splotch is a rendering algorithm for exploration and visual discovery in particle-based datasets coming from astronomical observations or numerical simulations. The strengths of the approach are production of high quality imagery and support for very large-scale datasets through an effective mix of the OpenMP and MPI parallel programming paradigms. This article reports our experiences in re-designing Splotch for exploiting emerging HPC architectures nowadays increasingly populated with GPUs. A performance model is introduced for data transfers, computations and memory access, to guide our re-factoring of Splotch. A number of parallelization issues are discussed, in particular relating to race conditions and workload balancing, towards achieving optimal performances. Our implementation was accomplished by using the CUDA programming paradigm. Our strategy is founded on novel schemes achieving optimized data organisation and classification of particles. We deploy a reference simulation to present performance results on acceleration gains and scalability. We finally outline our vision for future work developments including possibilities for further optimisations and exploitation of emerging technologies.Comment: 25 pages, 9 figures. Astronomy and Computing (2014

Scalability of Incompressible Flow Computations on Multi-GPU Clusters Using Dual-Level and Tri-Level Parallelism

High performance computing using graphics processing units (GPUs) is gaining popularity in the scientific computing field, with many large compute clusters being augmented with multiple GPUs in each node. We investigate hybrid tri-level (MPI-OpenMP-CUDA) parallel implementations to explore the efficiency and scalability of incompressible flow computations on GPU clusters up to 128 GPUS. This work details some of the unique issues faced when merging fine-grain parallelism on the GPU using CUDA with coarse-grain parallelism using OpenMP for intra-node and MPI for inter-node communication. Comparisons between the tri-level MPI-OpenMP-CUDA and dual-level MPI-CUDA implementations are shown using computationally large computational fluid dynamics (CFD) simulations. Our results demonstrate that a tri-level parallel implementation does not provide a significant advantage in performance over the dual-level implementation, however further research is needed to justify our conclusion for a cluster with a high GPU per node density or when using software that can utilize OpenMP’s fine-grain parallelism more effectively

Doctor of Philosophy in Computing

dissertationThe aim of direct volume rendering is to facilitate exploration and understanding of three-dimensional scalar fields referred to as volume datasets. Improving understanding is done by improving depth perception, whereas facilitating exploration is done by speeding up volume rendering. In this dissertation, improving both depth perception and rendering speed is considered. The impact of depth of field (DoF) on depth perception in direct volume rendering is evaluated by conducting a user study in which the test subjects had to choose which of two features, located at different depths, appeared to be in front in a volume-rendered image. Whereas DoF was expected to improve perception in all cases, the user study revealed that if used on the back feature, DoF reduced depth perception, whereas it produced a marked improvement when used on the front feature. We then worked on improving the speed of volume rendering on distributed memory machines. Distributed volume rendering has three stages: loading, rendering, and compositing. In this dissertation, the focus is on image compositing, more specifically, trying to optimize communication in image compositing algorithms. For that, we have developed the Task Overlapped Direct Send Tree image compositing algorithm, which works on both CPU- and GPU-accelerated supercomputers, which focuses on communication avoidance and overlapping communication with computation; the Dynamically Scheduled Region-Based image compositing algorithm that uses spatial and temporal awareness to efficiently schedule communication among compositing nodes, and a rendering and compositing pipeline that allows both image compositing and rendering to be done on GPUs of GPU-accelerated supercomputers. We tested these on CPU- and GPU-accelerated supercomputers and explain how these improvements allow us to obtain better performance than image compositing algorithms that focus on load-balancing and algorithms that have no spatial and temporal awareness of the rendering and compositing stages

Multi-Level Parallelism for Incompressible Flow Computations on GPU Clusters

We investigate multi-level parallelism on GPU clusters with MPI-CUDA and hybrid MPI-OpenMP-CUDA parallel implementations, in which all computations are done on the GPU using CUDA. We explore efficiency and scalability of incompressible flow computations using up to 256 GPUs on a problem with approximately 17.2 billion cells. Our work addresses some of the unique issues faced when merging fine-grain parallelism on the GPU using CUDA with coarse-grain parallelism that use either MPI or MPI-OpenMP for communications. We present three different strategies to overlap computations with communications, and systematically assess their impact on parallel performance on two different GPU clusters. Our results for strong and weak scaling analysis of incompressible flow computations demonstrate that GPU clusters offer significant benefits for large data sets, and a dual-level MPI-CUDA implementation with maximum overlapping of computation and communication provides substantial benefits in performance. We also find that our tri-level MPI-OpenMP-CUDA parallel implementation does not offer a significant advantage in performance over the dual-level implementation on GPU clusters with two GPUs per node, but on clusters with higher GPU counts per node or with different domain decomposition strategies a tri-level implementation may exhibit higher efficiency than a dual-level implementation and needs to be investigated further

High-Performance Computing: Dos and Don’ts

Computational fluid dynamics (CFD) is the main field of computational mechanics that has historically benefited from advances in high-performance computing. High-performance computing involves several techniques to make a simulation efficient and fast, such as distributed memory parallelism, shared memory parallelism, vectorization, memory access optimizations, etc. As an introduction, we present the anatomy of supercomputers, with special emphasis on HPC aspects relevant to CFD. Then, we develop some of the HPC concepts and numerical techniques applied to the complete CFD simulation framework: from preprocess (meshing) to postprocess (visualization) through the simulation itself (assembly and iterative solvers)